Publication Detail

Stochastic Mapping Frameworks.

Richard J. Rikoski, John Leonard, Paul Newman
2002
8 pp.
MITSG 03-18J
$3.50 (domestic); 5.50 (international). DOM
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URL: http://cml.mit.edu/~jleonard/pubs/rikoski_newman_leonard_paper816.pdf

Stochastic mapping is a powerful approach to concurrent mapping and localization because feature and robot states are explicitly correlated. Improving the estimate of any state automatically improves the estimates of correlated states. This paper describes a number of extensions to the stochastic mapping framework, which are made possible by the incorporation of past vehicle states into the state vector to explicitly represent the robot's trajectory.

type: Technical reports

Parent Project

Project No.: 1999-RCM-3
Title: Integrated Mapping and Navigation for Autonomous Underwater Vehicles